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<table width="100%" summary="page for terrorism"><tr><td>terrorism</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>
Global Terrorism Database yearly summaries
</h2>

<h3>Description</h3>

<p>The 
<a href="https://en.wikipedia.org/wiki/Global_Terrorism_Database">Global 
Terrorism Database (GTD)</a> 
&quot;is a database of incidents of terrorism 
from 1970 onward&quot;.  Through 2015, this 
database contains information on 141,966 
incidents.  
</p>
<p><code>terrorism</code> provides a few summary
statistics along with an <code>ordered</code>
factor <code>methodology</code>, which 
<a href="https://www.washingtonpost.com/news/monkey-cage/wp/2014/08/11/how-to-fix-the-flaws-in-the-global-terrorism-database-and-why-it-matters/">Pape et al.</a>
insisted is necessary, because an increase 
of over 70 percent in suicide terrorism 
between 2007 and 2013 is best explained by 
a methodology change in GTD that occurred 
on 2011-11-01;  Pape's own 
<a href="https://en.wikipedia.org/wiki/Suicide_Attack_Database">Suicide Attack Database</a> 
showed a 19 percent <em>decrease</em> over
the same period.  
</p>


<h3>Usage</h3>

<pre>
  data(terrorism)
  data(incidents.byCountryYr)
  data(nkill.byCountryYr)
</pre>


<h3>Format</h3>

<p><code>incidents.byCountryYr</code> and 
<code>nkill.byCountryYr</code> are matrices giving 
the numbers of incidents and numbers of deaths 
by year and by location of the event for 206
countries (rows) and for all years between 
1970 and 2016 (columns) except for 1993, for 
which the entries are all NA, because the raw
data previously collected was lost (though 
the total for that year is available in 
the <code>data.frame</code> 
<code>terrorism</code>). 
</p>
<p>NOTES:  
</p>
<p>1.  For <code>nkill.byCountryYr</code> and for 
<code>terrorism[c('nkill', 'nkill.us')]</code>, NAs 
in GTD were treated as 0.  Thus the actual 
number of deaths were likely higher, unless 
this was more than offset by incidents being 
classified as terrorism, when they should not
have been.  
</p>
<p>2.  <code>incidents.byCountryYr</code> and 
<code>nkill.byCountryYr</code> are NA for 1993,
because the GTD data for that year were lost.  
</p>
<p><code>terrorism</code> is a <code>data.frame</code>
containing the following:  
</p>

<dl>
<dt>year</dt><dd><p>integer year, 1970:2016.</p>
</dd>
<dt>methodology</dt><dd>
<p>an <code>ordered</code> factor giving the 
methodology / organization responsible for 
the data collection for most of the given
year.  The Pinkerton Global Intelligence 
Service (<code>PGIS</code>) managed data collection 
from 1970-01-01 to 1997-12-31.  The 
Center for Terrorism and Intelligence 
Studies (<code>CETIS</code>) managed the project 
from 1998-01-01 to 2008-03-31.  The 
Institute for the Study of Violent Groups 
(<code>ISVG</code>) carried the project from 
2008-04-01 to 2011-10-31.  The National 
Consortium for the Study of Terrorism and
Responses to Terrorism (<code>START</code>) has
managed data collection since 
2011-11-01.  For this variable, 
partial years are ignored, so 
<code>methodology</code> = CEDIS for 
1998:2007, ISVG for 2008:2011, and 
START for 2012:2014. 
</p>
</dd>
<dt>method</dt><dd>
<p>a character vector consisting of 
the first character of the levels 
of <code>methodology</code>:  
</p>
<p>c('p', 'c', 'i', 's')
</p>
</dd>
<dt>incidents</dt><dd>
<p>integer number of incidents identified 
each year.  
</p>
<p>NOTE:  
<code>sum(terrorism[["incidents"]])</code> =
146920 = 141966 in the GTD database
plus 4954 for 1993, for which the
incident-level data were lost.  
</p>
</dd>
<dt>incidents.us</dt><dd>
<p>integer number of incidents identified 
each year with <code>country_txt</code> = 
&quot;United States&quot;.  
</p>
</dd>
<dt>suicide</dt><dd>
<p>integer number of incidents classified
as &quot;suicide&quot; by GTD variable 
<code>suicide</code> = 1.  For 2007, this 
is 359, the number reported by 
<a href="https://www.washingtonpost.com/news/monkey-cage/wp/2014/08/11/how-to-fix-the-flaws-in-the-global-terrorism-database-and-why-it-matters/">Pape et al.</a>  
For 2013, it is 624, which is 5 more 
than the 619 mentioned by Pape et al.  
Without checking with the SMART 
project administrators, one might 
suspect that 5 more suicide incidents 
from 2013 were found after the data
Pape et al. analyzed but before the 
data used for this analysis.  
</p>
</dd>
<dt>suicide.us</dt><dd>
<p>Number of suicide incidents by year 
with  <code>country_txt</code> = 
&quot;United States&quot;. 
</p>
</dd> 
<dt>nkill</dt><dd>
<p>number of confirmed fatalities for
incidents in the given year, including 
attackers = 
<code>sum(nkill, na.rm=TRUE)</code> in the 
GTD incident data.  
</p>
<p>NOTE:  <code>nkill</code> in the GTD incident
data includes both perpetrators
and victims when both are available.  
It includes one when only one is
available and is <code>NA</code> when 
neither is available. However, in 
most cases, we might expect that the 
more spectacular and lethal incidents 
would likely be more accurately 
reported. To the extent that this is 
true, it means that when numbers are
missing, they are usually zero or 
small.  This further suggests that 
the summary numbers recorded here 
probably represent a slight but not
substantive undercount.  
</p>
</dd>
<dt>nkill.us</dt><dd>
<p>number of U.S. citizens who died as a
result of incidents for that year = 
<code>sum(nkill.us, na.rm=TRUE)</code> in the 
GTD incident data.  
</p>
<p>NOTES:  
</p>
<p>1.  This is subject to the same likely
modest undercount discussed with 
<code>nkill</code>.)
</p>
<p>2.  These are U.S. citizens killed 
regardless of location.  This explains at
least part of the discrepancies between
<code>terrorism[, 'nkill.us']</code> and 
<code>nkill.byCountryYr['United States', ]</code>.
</p>
</dd>
<dt>nwound</dt><dd>
<p>number of people wounded.  (This is
subject to the same likely modest
undercount discussed with 
<code>nkill</code>.)
</p>
</dd>
<dt>nwound.us</dt><dd>
<p>Number of U.S. citizens wounded in
terrorist incidents for that year = 
<code>sum(nwound.us, na.rm=TRUE)</code> in 
the GTD incident data.  (This is
subject to the same likely modest
undercount discussed with 
<code>nkill</code>.)
</p>
</dd>
<dt>pNA.nkill, pNA.nkill.us, 
pNA.nwound, pNA.nwound.us</dt><dd>
<p>proportion of observations by year
with missing values.  These numbers
are higher for the early data than 
more recent numbers.  This is 
particularly true for <code>nkill.us</code>
and <code>nwound.us</code>, which exceed 
90 percent for most of the period 
with <code>methodology</code> = 'PGIS', 
prior to 1998.  
</p>
</dd>
<dt>worldPopulation, USpopulation</dt><dd>
<p>Estimated de facto population in thousands 
living in the world and in the US as of 1 
July of the year indicated, according to 
the Population Division of the Department 
of Economic and Social Affairs of the 
United Nations;  see &quot;Sources&quot; below.  
</p>
</dd>
<dt>worldDeathRate, USdeathRate</dt><dd>
<p><a href="https://en.wikipedia.org/wiki/Mortality_rate">Crude death rate</a>
(deaths per 1,000 population) worldwide 
and in the US, according to the World 
Bank;  see &quot;Sources&quot; below.  This World
Bank data set includes <code>USdeathRate</code> 
for each year from 1900 to 2014.  
</p>
<p>The <code>worldDeathRate</code> numbers here were 
read manually from a plot on  
<a href="https://data.worldbank.org/indicator/SP.DYN.CDRT.IN?end=2014&amp;start=1960&amp;view=chart">that web page,</a> 
except for the the number for 2015, which was 
estimated as a reduction of 0.73 percent from 
2014, which was the average rate of decline 
(ratio of two successive years) for 1990 to 
2014.  The same method was used to estimate 
the <code>USdeathRate</code> for 2015 as the same 
as for 2014.  
</p>
<p>NOTE:  <code>USdeathRate</code> is to two 
significant digits only, unlike 
<code>worldDeathRate</code>, which has four 
significant digits. 
</p>
</dd>
<dt>worldDeaths, USdeaths</dt><dd>
<p>number of deaths by year in the world and 
US
</p>
<p><code>worldDeaths = 
         worldPopulation * worldDeathRate</code>.  
</p>
<p><code>USdeaths</code> were computed by summing 
across age groups in &quot;Deaths_5x1.txt&quot; for 
the United States, downloaded from 
<a href="http://www.mortality.org/cgi-bin/hmd/country.php?cntr=USA&amp;level=1">http://www.mortality.org/cgi-bin/hmd/country.php?cntr=USA&amp;level=1</a> from the Human 
Mortality Database;  see sources below.  
</p>
</dd>
<dt>kill.pmp, kill.pmp.us</dt><dd>
<p>terrorism deaths per million 
population worldwide and in the US = 
</p>
<p><code>0.001 * nkill / worldPopulation</code>
</p>
</dd>
<dt>pkill, pkill.us</dt><dd>
<p>terrorism deaths as a proportion of 
total deaths worldwide and in the US
</p>
<p><code>pkill = nkill / worldDeaths</code> 
</p>
<p><code>pkill.us = nkill.us / USdeaths</code> 
</p>
</dd>
</dl>



<h3>Details</h3>

<p>As noted with the &quot;description&quot; above, 
<a href="https://www.washingtonpost.com/news/monkey-cage/wp/2014/08/11/how-to-fix-the-flaws-in-the-global-terrorism-database-and-why-it-matters/">Pape et al.</a> 
noted that the GTD reported an increase in
suicide terrorism of over 70 percent
between 2007 and 2013, while their <a href="https://en.wikipedia.org/wiki/Suicide_Attack_Database">Suicide Attack Database</a>
showed a 19 percent <em>decrease</em> over
the same period.  Pape et al. insisted that
the most likely explanation for this 
difference is the change in the 
organization responsible for managing 
that data collection from ISVG to START.  
</p>
<p>If the issue is restricted to how 
incidents are classified as &quot;suicide 
terrorism&quot;, this concern does not affect 
the other variables in this summary.  
</p>
<p>However, if it also impacts what 
incidents are classified as &quot;terrorism&quot;, 
it suggests larger problems.  
</p>


<h3>Author(s)</h3>

<p>Spencer Graves</p>


<h3>Source</h3>

<p>National Consortium for the Study of Terrorism and Responses to Terrorism (START). (2017). Global Terrorism Database [Data file]. Retrieved from <a href="http://www.start.umd.edu/gtd">http://www.start.umd.edu/gtd</a> [accessed 2018-04-08]. 
</p>
<p>See also the <a href="https://en.wikipedia.org/wiki/Global_Terrorism_Database">Global Terrorism Database</a> maintained by the <a href="https://en.wikipedia.org/wiki/National_Consortium_for_the_Study_of_Terrorism_and_Responses_to_Terrorism">National Consortium for the Study of Terrorism and Responses to Terrorism</a> (START, 2015), <a href="http://www.start.umd.edu/gtd">http://www.start.umd.edu/gtd</a>. 
</p>
<p>The world and US population figures came from 
&quot;Total Population - Both Sexes&quot;, World Population 
Prospects 2015, published by the Population Division 
of the Department of Economic and Social Affairs of 
the United Nations accessed 2016-09-05 (at a web link 
that has since changed:  No longer at 
<code>https://esa.un.org/unpd/wpp/Download/Standard/Population</code>, as it was when the data current used here was 
downloaded, 2016-09-05.  Fortunately, as of 
2020-02-09, such data seem to be available at 
<a href="https://population.un.org/wpp/Download/Standard/Population/">https://population.un.org/wpp/Download/Standard/Population/</a>.   
</p>
<p><a href="http://www.mortality.org/">Human Mortality Database.  University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany).</a>
</p>


<h3>References</h3>

<p>Robert Pape, Keven Ruby, Vincent Bauer and 
Gentry Jenkins, <a href="https://www.washingtonpost.com/news/monkey-cage/wp/2014/08/11/how-to-fix-the-flaws-in-the-global-terrorism-database-and-why-it-matters/">&quot;How to fix
the flaws in the Global Terrorism Database
and why it matters&quot;</a>, 
<em>The Washington Post</em>, August 11, 
2014 (accessed 2016-01-09).  
</p>


<h3>Examples</h3>

<pre>
data(terrorism)
##
## plot deaths per million population 
##
plot(kill.pmp~year, terrorism, 
     pch=method, type='b')
plot(kill.pmp.us~year, terrorism, 
     pch=method, type='b', 
     log='y', las=1)
     
# terrorism as parts per 10,000 
# of all deaths 

plot(pkill*1e4~year, terrorism, 
     pch=method, type='b', 
     las=1)
plot(pkill.us*1e4~year, terrorism, 
     pch=method, type='b', 
     log='y', las=1)
     
# plot number of incidents, number killed, 
# and proportion NA

plot(incidents~year, terrorism, type='b', 
      pch=method)

plot(nkill.us~year, terrorism, type='b', 
      pch=method)
plot(nkill.us~year, terrorism, type='b', 
      pch=method, log='y')

plot(pNA.nkill.us~year, terrorism, type='b', 
      pch=method)
abline(v=1997.5, lty='dotted', col='red')

##
## by country by year
##
data(incidents.byCountryYr)
data(nkill.byCountryYr)

yr &lt;- as.integer(colnames(
  incidents.byCountryYr))
str(maxDeaths &lt;- apply(nkill.byCountryYr, 
                       1, max) )
str(omax &lt;- order(maxDeaths, decreasing=TRUE))
head(maxDeaths[omax], 8)
tolower(substring( 
  names(maxDeaths[omax[1:8]]), 1, 2))
pch. &lt;- c('i', 'g', 'f', 'l', 
          's', 'c', 'u', 'p')
cols &lt;- 1:4

matplot(yr, sqrt(t(
  nkill.byCountryYr[omax[1:8], ])),
  type='b', pch=pch., axes=FALSE, 
  ylab='(square root scale)   ', xlab='', 
  col=cols,
  main='number of terrorism deaths\nby country') 
axis(1)
(max.nk &lt;- max(nkill.byCountryYr[omax[1:8], ]))
i.nk &lt;- c(1, 100, 1000, 3000, 
          5000, 7000, 10000)
cbind(i.nk, sqrt(i.nk))
axis(2, sqrt(i.nk), i.nk, las=1)
ip &lt;- paste(pch., names(maxDeaths[omax[1:8]]))
legend('topleft', ip, cex=.55, 
       col=cols, text.col=cols)
</pre>


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